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Atomizer/CHANGELOG.md
Anto01 0a7cca9c6a feat: Complete Phase 2.5-2.7 - Intelligent LLM-Powered Workflow Analysis 
This commit implements three major architectural improvements to transform
Atomizer from static pattern matching to intelligent AI-powered analysis.

## Phase 2.5: Intelligent Codebase-Aware Gap Detection ✅

Created intelligent system that understands existing capabilities before
requesting examples:

**New Files:**
- optimization_engine/codebase_analyzer.py (379 lines)
  Scans Atomizer codebase for existing FEA/CAE capabilities

- optimization_engine/workflow_decomposer.py (507 lines, v0.2.0)
  Breaks user requests into atomic workflow steps
  Complete rewrite with multi-objective, constraints, subcase targeting

- optimization_engine/capability_matcher.py (312 lines)
  Matches workflow steps to existing code implementations

- optimization_engine/targeted_research_planner.py (259 lines)
  Creates focused research plans for only missing capabilities

**Results:**
- 80-90% coverage on complex optimization requests
- 87-93% confidence in capability matching
- Fixed expression reading misclassification (geometry vs result_extraction)

## Phase 2.6: Intelligent Step Classification ✅

Distinguishes engineering features from simple math operations:

**New Files:**
- optimization_engine/step_classifier.py (335 lines)

**Classification Types:**
1. Engineering Features - Complex FEA/CAE needing research
2. Inline Calculations - Simple math to auto-generate
3. Post-Processing Hooks - Middleware between FEA steps

## Phase 2.7: LLM-Powered Workflow Intelligence ✅

Replaces static regex patterns with Claude AI analysis:

**New Files:**
- optimization_engine/llm_workflow_analyzer.py (395 lines)
  Uses Claude API for intelligent request analysis
  Supports both Claude Code (dev) and API (production) modes

- .claude/skills/analyze-workflow.md
  Skill template for LLM workflow analysis integration

**Key Breakthrough:**
- Detects ALL intermediate steps (avg, min, normalization, etc.)
- Understands engineering context (CBUSH vs CBAR, directions, metrics)
- Distinguishes OP2 extraction from part expression reading
- Expected 95%+ accuracy with full nuance detection

## Test Coverage

**New Test Files:**
- tests/test_phase_2_5_intelligent_gap_detection.py (335 lines)
- tests/test_complex_multiobj_request.py (130 lines)
- tests/test_cbush_optimization.py (130 lines)
- tests/test_cbar_genetic_algorithm.py (150 lines)
- tests/test_step_classifier.py (140 lines)
- tests/test_llm_complex_request.py (387 lines)

All tests include:
- UTF-8 encoding for Windows console
- atomizer environment (not test_env)
- Comprehensive validation checks

## Documentation

**New Documentation:**
- docs/PHASE_2_5_INTELLIGENT_GAP_DETECTION.md (254 lines)
- docs/PHASE_2_7_LLM_INTEGRATION.md (227 lines)
- docs/SESSION_SUMMARY_PHASE_2_5_TO_2_7.md (252 lines)

**Updated:**
- README.md - Added Phase 2.5-2.7 completion status
- DEVELOPMENT_ROADMAP.md - Updated phase progress

## Critical Fixes

1. **Expression Reading Misclassification** (lines cited in session summary)
   - Updated codebase_analyzer.py pattern detection
   - Fixed workflow_decomposer.py domain classification
   - Added capability_matcher.py read_expression mapping

2. **Environment Standardization**
   - All code now uses 'atomizer' conda environment
   - Removed test_env references throughout

3. **Multi-Objective Support**
   - WorkflowDecomposer v0.2.0 handles multiple objectives
   - Constraint extraction and validation
   - Subcase and direction targeting

## Architecture Evolution

**Before (Static & Dumb):**
User Request → Regex Patterns → Hardcoded Rules → Missed Steps ❌

**After (LLM-Powered & Intelligent):**
User Request → Claude AI Analysis → Structured JSON →
├─ Engineering (research needed)
├─ Inline (auto-generate Python)
├─ Hooks (middleware scripts)
└─ Optimization (config) ✅

## LLM Integration Strategy

**Development Mode (Current):**
- Use Claude Code directly for interactive analysis
- No API consumption or costs
- Perfect for iterative development

**Production Mode (Future):**
- Optional Anthropic API integration
- Falls back to heuristics if no API key
- For standalone batch processing

## Next Steps

- Phase 2.8: Inline Code Generation
- Phase 2.9: Post-Processing Hook Generation
- Phase 3: MCP Integration for automated documentation research

🚀 Generated with Claude Code

Co-Authored-By: Claude <noreply@anthropic.com>
2025-11-16 13:35:41 -05:00

3.7 KiB
Raw Blame History

Changelog

All notable changes to Atomizer will be documented in this file.

The format is based on Keep a Changelog.

[Unreleased]

Phase 2 - LLM Integration (In Progress)

  • Natural language interface for optimization configuration
  • Feature registry with capability catalog
  • Claude skill for Atomizer navigation

[0.2.0] - 2025-01-16

Phase 1 - Plugin System & Infrastructure ✅

Added

  • Plugin Architecture

    • Hook manager with lifecycle execution at pre_solve, post_solve, and post_extraction points
    • Plugin auto-discovery from optimization_engine/plugins/ directory
    • Priority-based hook execution
    • Context passing system for hooks (output_dir, trial_number, design_variables, results)

  • Logging Infrastructure

    • Detailed per-trial logs in optimization_results/trial_logs/
      • Complete iteration trace with timestamps
      • Design variables, configuration, execution timeline
      • Extracted results and constraint evaluations
    • High-level optimization progress log (optimization.log)
      • Configuration summary header
      • Trial START and COMPLETE entries (one line per trial)
      • Compact format for easy progress monitoring

  • Logging Plugins

    • detailed_logger.py - Creates detailed trial logs
    • optimization_logger.py - Creates high-level optimization.log
    • log_solve_complete.py - Appends solve completion to trial logs
    • log_results.py - Appends extracted results to trial logs
    • optimization_logger_results.py - Appends results to optimization.log

  • Project Organization

    • Studies folder structure with standardized layout
    • Comprehensive studies documentation (studies/README.md)
    • Model files organized in model/ subdirectory (.prt, .sim, .fem)
    • Intelligent path resolution system (atomizer_paths.py)
    • Marker-based project root detection

  • Test Suite

    • test_hooks_with_bracket.py - Hook validation test (3 trials)
    • run_5trial_test.py - Quick integration test (5 trials)
    • test_journal_optimization.py - Full optimization test

Changed

  • Renamed examples/ folder to studies/
  • Moved bracket example to studies/bracket_stress_minimization/
  • Consolidated FEA files into model/ subfolder
  • Updated all test scripts to use atomizer_paths for imports
  • Runner now passes output_dir to all hook contexts

Removed

  • Obsolete test scripts from examples/ (14 files deleted)
  • optimization_logs/ and optimization_results/ from root directory

Fixed

  • Log files now correctly generated in study-specific optimization_results/ folder
  • Path resolution works regardless of script location
  • Hooks properly registered with register_hooks() function

[0.1.0] - 2025-01-10

Initial Release

Core Features

  • Optuna integration with TPE sampler
  • NX journal integration for expression updates and simulation execution
  • OP2 result extraction (stress, displacement)
  • Study management with folder-based isolation
  • Web dashboard for real-time monitoring
  • Precision control (4-decimal rounding for mm/degrees/MPa)
  • Crash recovery and optimization resumption

Development Timeline

  • Phase 1 (✅ Completed 2025-01-16): Plugin system & hooks
  • Phase 2 (🟡 Starting): LLM interface with natural language configuration
  • Phase 3 (Planned): Dynamic code generation for custom objectives
  • Phase 4 (Planned): Intelligent analysis and surrogate quality assessment
  • Phase 5 (Planned): Automated HTML/PDF report generation
  • Phase 6 (Planned): NX MCP server with full API documentation
  • Phase 7 (Planned): Self-improving feature registry

Maintainer: Antoine Polvé (antoine@atomaste.com) License: Proprietary - Atomaste © 2025

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